code: def _execute_map(cls, ctx, op: "DataFrameGroupbySortShuffle"):
      df, pivots = [ctx[c.key] for c in op.inputs]
      out = op.outputs[0]
  
      def _get_out_df(p_index, in_df):
          if p_index == 0:
              out_df = in_df.loc[: pivots[p_index]]
          elif p_index == op.n_partition - 1:
              out_df = in_df.loc[pivots[p_index - 1] :].drop(
                  index=pivots[p_index - 1], errors="ignore"
              )
          else:
              out_df = in_df.loc[
                  pivots[p_index - 1] : pivots[p_index]
              ].drop(index=pivots[p_index - 1], errors="ignore")
          return out_df
  
      for i in range(op.n_partition):
          index = (i, 0)
          out_df = tuple(_get_out_df(i, x) for x in df)
          ctx[out.key, index] = out_df
  
imports: import numpy
  import pandas
  from ...None import opcodes
  from ...core import OutputType
  from ...core.operand import MapReduceOperand, OperandStage
  from ...serialization.serializables import Int32Field, ListField
  from ...utils import lazy_import
  from ..operands import DataFrameOperandMixin
  from ..sort.psrs import DataFramePSRSChunkOperand
label_desc: Use image node_id 3 for calling the DataFrameGroupbySortShuffle obj's underlying member method code with example usage: obj._execute_map(cls, ctx, op) and returns: out_df